Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 65, Issue 7, Pages 5461-5473Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2015.2453391
Keywords
Adaptive filters; coverage estimation; Kernel-based filtering; machine learning; mobile communications
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Funding
- Alcatel-Lucent within Project PreReAl2
- European Commission [FP7 ICT-317669 METIS]
- KDDI Foundation
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In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected sub-gradient method (APSM) and a state-of-the-art adaptive multi-kernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.
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